Overview

Dataset statistics

Number of variables31
Number of observations3305
Missing cells827
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory710.2 KiB
Average record size in memory220.0 B

Variable types

Categorical4
Text3
Numeric20
Boolean4

Alerts

councildistrictcode is highly overall correlated with latitude and 1 other fieldsHigh correlation
propertygfatotal is highly overall correlated with propertygfabuilding_s and 4 other fieldsHigh correlation
propertygfabuilding_s is highly overall correlated with propertygfatotal and 4 other fieldsHigh correlation
largestpropertyusetypegfa is highly overall correlated with propertygfatotal and 4 other fieldsHigh correlation
energystarscore is highly overall correlated with sourceeui_kbtu_sf and 1 other fieldsHigh correlation
siteeui_kbtu_sf is highly overall correlated with siteeuiwn_kbtu_sf and 5 other fieldsHigh correlation
siteeuiwn_kbtu_sf is highly overall correlated with siteeui_kbtu_sf and 5 other fieldsHigh correlation
sourceeui_kbtu_sf is highly overall correlated with energystarscore and 5 other fieldsHigh correlation
sourceeuiwn_kbtu_sf is highly overall correlated with energystarscore and 5 other fieldsHigh correlation
siteenergyuse_kbtu is highly overall correlated with propertygfatotal and 8 other fieldsHigh correlation
siteenergyusewn_kbtu is highly overall correlated with propertygfatotal and 8 other fieldsHigh correlation
totalghgemissions is highly overall correlated with propertygfatotal and 7 other fieldsHigh correlation
latitude is highly overall correlated with councildistrictcode and 1 other fieldsHigh correlation
source_site is highly overall correlated with totalghgemissions and 2 other fieldsHigh correlation
buildingtype is highly overall correlated with primarypropertytype and 2 other fieldsHigh correlation
primarypropertytype is highly overall correlated with buildingtype and 2 other fieldsHigh correlation
neighborhood is highly overall correlated with councildistrictcode and 1 other fieldsHigh correlation
electricity is highly overall correlated with source_siteHigh correlation
naturalgas is highly overall correlated with source_siteHigh correlation
defaultdata is highly overall correlated with buildingtype and 2 other fieldsHigh correlation
compliancestatus is highly overall correlated with buildingtype and 2 other fieldsHigh correlation
steam is highly imbalanced (76.6%)Imbalance
electricity is highly imbalanced (98.7%)Imbalance
defaultdata is highly imbalanced (78.9%)Imbalance
compliancestatus is highly imbalanced (78.9%)Imbalance
energystarscore has 805 (24.4%) missing valuesMissing
totalghgemissions is highly skewed (γ1 = 20.57060096)Skewed
siteenergyuse_kbtu has unique valuesUnique
numberofbuildings has 92 (2.8%) zerosZeros
propertygfaparking has 2811 (85.1%) zerosZeros

Reproduction

Analysis started2023-08-03 20:59:35.562448
Analysis finished2023-08-03 21:01:11.659226
Duration1 minute and 36.1 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

buildingtype
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
NonResidential
1432 
Multifamily LR (1-4)
997 
Multifamily MR (5-9)
577 
Multifamily HR (10+)
 
108
Nonresidential COS
 
84
Other values (3)
 
107

Length

Max length20
Median length20
Mean length17.175794
Min length6

Characters and Unicode

Total characters56766
Distinct characters40
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNonResidential
2nd rowNonResidential
3rd rowNonResidential
4th rowNonResidential
5th rowNonResidential

Common Values

ValueCountFrequency (%)
NonResidential 1432
43.3%
Multifamily LR (1-4) 997
30.2%
Multifamily MR (5-9) 577
17.5%
Multifamily HR (10+) 108
 
3.3%
Nonresidential COS 84
 
2.5%
SPS-District K-12 83
 
2.5%
Campus 23
 
0.7%
Nonresidential WA 1
 
< 0.1%

Length

2023-08-03T23:01:11.763443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T23:01:11.984596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
multifamily 1682
24.6%
nonresidential 1517
22.2%
lr 997
14.6%
1-4 997
14.6%
mr 577
 
8.4%
5-9 577
 
8.4%
hr 108
 
1.6%
10 108
 
1.6%
cos 84
 
1.2%
sps-district 83
 
1.2%
Other values (3) 107
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i 6564
 
11.6%
l 4881
 
8.6%
3532
 
6.2%
t 3365
 
5.9%
a 3222
 
5.7%
R 3114
 
5.5%
n 3034
 
5.3%
e 3034
 
5.3%
M 2259
 
4.0%
- 1740
 
3.1%
Other values (30) 22021
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35805
63.1%
Uppercase Letter 8687
 
15.3%
Space Separator 3532
 
6.2%
Decimal Number 3530
 
6.2%
Dash Punctuation 1740
 
3.1%
Open Punctuation 1682
 
3.0%
Close Punctuation 1682
 
3.0%
Math Symbol 108
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6564
18.3%
l 4881
13.6%
t 3365
9.4%
a 3222
9.0%
n 3034
8.5%
e 3034
8.5%
u 1705
 
4.8%
m 1705
 
4.8%
f 1682
 
4.7%
y 1682
 
4.7%
Other values (6) 4931
13.8%
Uppercase Letter
ValueCountFrequency (%)
R 3114
35.8%
M 2259
26.0%
N 1517
17.5%
L 997
 
11.5%
S 250
 
2.9%
H 108
 
1.2%
C 107
 
1.2%
O 84
 
1.0%
P 83
 
1.0%
D 83
 
1.0%
Other values (3) 85
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1188
33.7%
4 997
28.2%
5 577
16.3%
9 577
16.3%
0 108
 
3.1%
2 83
 
2.4%
Space Separator
ValueCountFrequency (%)
3532
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1740
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1682
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1682
100.0%
Math Symbol
ValueCountFrequency (%)
+ 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44492
78.4%
Common 12274
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6564
14.8%
l 4881
11.0%
t 3365
 
7.6%
a 3222
 
7.2%
R 3114
 
7.0%
n 3034
 
6.8%
e 3034
 
6.8%
M 2259
 
5.1%
u 1705
 
3.8%
m 1705
 
3.8%
Other values (19) 11609
26.1%
Common
ValueCountFrequency (%)
3532
28.8%
- 1740
14.2%
( 1682
13.7%
) 1682
13.7%
1 1188
 
9.7%
4 997
 
8.1%
5 577
 
4.7%
9 577
 
4.7%
0 108
 
0.9%
+ 108
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6564
 
11.6%
l 4881
 
8.6%
3532
 
6.2%
t 3365
 
5.9%
a 3222
 
5.7%
R 3114
 
5.5%
n 3034
 
5.3%
e 3034
 
5.3%
M 2259
 
4.0%
- 1740
 
3.1%
Other values (30) 22021
38.8%

primarypropertytype
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
Low-Rise Multifamily
967 
Mid-Rise Multifamily
560 
Small- and Mid-Sized Office
288 
Other
250 
Warehouse
187 
Other values (19)
1053 

Length

Max length27
Median length22
Mean length17.229349
Min length5

Characters and Unicode

Total characters56943
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel

Common Values

ValueCountFrequency (%)
Low-Rise Multifamily 967
29.3%
Mid-Rise Multifamily 560
16.9%
Small- and Mid-Sized Office 288
 
8.7%
Other 250
 
7.6%
Warehouse 187
 
5.7%
Large Office 166
 
5.0%
Mixed Use Property 132
 
4.0%
K-12 School 123
 
3.7%
High-Rise Multifamily 103
 
3.1%
Retail Store 89
 
2.7%
Other values (14) 440
13.3%

Length

2023-08-03T23:01:12.201845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily 1630
23.7%
low-rise 967
14.1%
mid-rise 560
 
8.1%
office 496
 
7.2%
small 288
 
4.2%
and 288
 
4.2%
mid-sized 288
 
4.2%
other 250
 
3.6%
warehouse 199
 
2.9%
large 166
 
2.4%
Other values (28) 1741
25.3%

Most occurring characters

ValueCountFrequency (%)
i 7480
 
13.1%
e 4468
 
7.8%
l 4342
 
7.6%
3568
 
6.3%
a 2996
 
5.3%
t 2751
 
4.8%
f 2662
 
4.7%
M 2649
 
4.7%
- 2357
 
4.1%
s 2148
 
3.8%
Other values (33) 21522
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42241
74.2%
Uppercase Letter 8491
 
14.9%
Space Separator 3568
 
6.3%
Dash Punctuation 2357
 
4.1%
Decimal Number 246
 
0.4%
Other Punctuation 40
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7480
17.7%
e 4468
10.6%
l 4342
10.3%
a 2996
 
7.1%
t 2751
 
6.5%
f 2662
 
6.3%
s 2148
 
5.1%
o 2057
 
4.9%
m 2048
 
4.8%
u 1979
 
4.7%
Other values (14) 9310
22.0%
Uppercase Letter
ValueCountFrequency (%)
M 2649
31.2%
R 1766
20.8%
L 1143
13.5%
S 969
 
11.4%
O 746
 
8.8%
W 266
 
3.1%
H 212
 
2.5%
U 154
 
1.8%
C 143
 
1.7%
P 132
 
1.6%
Other values (4) 311
 
3.7%
Decimal Number
ValueCountFrequency (%)
1 123
50.0%
2 123
50.0%
Space Separator
ValueCountFrequency (%)
3568
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2357
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50732
89.1%
Common 6211
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7480
14.7%
e 4468
 
8.8%
l 4342
 
8.6%
a 2996
 
5.9%
t 2751
 
5.4%
f 2662
 
5.2%
M 2649
 
5.2%
s 2148
 
4.2%
o 2057
 
4.1%
m 2048
 
4.0%
Other values (28) 17131
33.8%
Common
ValueCountFrequency (%)
3568
57.4%
- 2357
37.9%
1 123
 
2.0%
2 123
 
2.0%
/ 40
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7480
 
13.1%
e 4468
 
7.8%
l 4342
 
7.6%
3568
 
6.3%
a 2996
 
5.3%
t 2751
 
4.8%
f 2662
 
4.7%
M 2649
 
4.7%
- 2357
 
4.1%
s 2148
 
3.8%
Other values (33) 21522
37.8%
Distinct3202
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:12.571820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length10
Mean length10.005144
Min length9

Characters and Unicode

Total characters33067
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3126 ?
Unique (%)94.6%

Sample

1st row0659000030
2nd row0659000220
3rd row0659000475
4th row0659000640
5th row0659000970
ValueCountFrequency (%)
0002400002 5
 
0.2%
0925049346 5
 
0.2%
3224049012 5
 
0.2%
1625049001 5
 
0.2%
3624039009 4
 
0.1%
7666203240 4
 
0.1%
3224049007 3
 
0.1%
8809700040 3
 
0.1%
0164000222 3
 
0.1%
7954000005 3
 
0.1%
Other values (3193) 3267
98.8%
2023-08-03T23:01:13.140696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11105
33.6%
2 3099
 
9.4%
5 2883
 
8.7%
6 2663
 
8.1%
1 2627
 
7.9%
9 2337
 
7.1%
7 2325
 
7.0%
4 2122
 
6.4%
3 2023
 
6.1%
8 1876
 
5.7%
Other values (5) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33060
> 99.9%
Lowercase Letter 3
 
< 0.1%
Space Separator 2
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11105
33.6%
2 3099
 
9.4%
5 2883
 
8.7%
6 2663
 
8.1%
1 2627
 
7.9%
9 2337
 
7.1%
7 2325
 
7.0%
4 2122
 
6.4%
3 2023
 
6.1%
8 1876
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
a 1
33.3%
n 1
33.3%
d 1
33.3%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33064
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11105
33.6%
2 3099
 
9.4%
5 2883
 
8.7%
6 2663
 
8.1%
1 2627
 
7.9%
9 2337
 
7.1%
7 2325
 
7.0%
4 2122
 
6.4%
3 2023
 
6.1%
8 1876
 
5.7%
Other values (2) 4
 
< 0.1%
Latin
ValueCountFrequency (%)
a 1
33.3%
n 1
33.3%
d 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11105
33.6%
2 3099
 
9.4%
5 2883
 
8.7%
6 2663
 
8.1%
1 2627
 
7.9%
9 2337
 
7.1%
7 2325
 
7.0%
4 2122
 
6.4%
3 2023
 
6.1%
8 1876
 
5.7%
Other values (5) 7
 
< 0.1%

councildistrictcode
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4417549
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:13.322344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1181104
Coefficient of variation (CV)0.47686341
Kurtosis-1.4458649
Mean4.4417549
Median Absolute Deviation (MAD)2
Skewness-0.06926434
Sum14680
Variance4.4863916
MonotonicityNot monotonic
2023-08-03T23:01:13.458465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 1015
30.7%
3 585
17.7%
2 502
15.2%
4 357
 
10.8%
5 333
 
10.1%
1 271
 
8.2%
6 242
 
7.3%
ValueCountFrequency (%)
1 271
 
8.2%
2 502
15.2%
3 585
17.7%
4 357
 
10.8%
5 333
 
10.1%
6 242
 
7.3%
7 1015
30.7%
ValueCountFrequency (%)
7 1015
30.7%
6 242
 
7.3%
5 333
 
10.1%
4 357
 
10.8%
3 585
17.7%
2 502
15.2%
1 271
 
8.2%

neighborhood
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
DOWNTOWN
557 
EAST
448 
MAGNOLIA / QUEEN ANNE
415 
GREATER DUWAMISH
371 
NORTHEAST
270 
Other values (8)
1244 

Length

Max length21
Median length10
Mean length10.118306
Min length4

Characters and Unicode

Total characters33441
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN

Common Values

ValueCountFrequency (%)
DOWNTOWN 557
16.9%
EAST 448
13.6%
MAGNOLIA / QUEEN ANNE 415
12.6%
GREATER DUWAMISH 371
11.2%
NORTHEAST 270
8.2%
LAKE UNION 250
7.6%
NORTHWEST 218
 
6.6%
NORTH 183
 
5.5%
SOUTHWEST 156
 
4.7%
BALLARD 131
 
4.0%
Other values (3) 306
9.3%

Length

2023-08-03T23:01:13.639204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown 557
10.8%
east 448
 
8.7%
magnolia 415
 
8.0%
415
 
8.0%
queen 415
 
8.0%
anne 415
 
8.0%
greater 371
 
7.2%
duwamish 371
 
7.2%
northeast 270
 
5.2%
union 250
 
4.8%
Other values (8) 1244
24.1%

Most occurring characters

ValueCountFrequency (%)
N 4073
12.2%
E 3719
11.1%
A 3439
10.3%
T 3163
9.5%
O 2700
 
8.1%
1866
 
5.6%
W 1859
 
5.6%
S 1807
 
5.4%
R 1756
 
5.3%
H 1292
 
3.9%
Other values (11) 7767
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31160
93.2%
Space Separator 1866
 
5.6%
Other Punctuation 415
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 4073
13.1%
E 3719
11.9%
A 3439
11.0%
T 3163
10.2%
O 2700
8.7%
W 1859
 
6.0%
S 1807
 
5.8%
R 1756
 
5.6%
H 1292
 
4.1%
U 1286
 
4.1%
Other values (9) 6066
19.5%
Space Separator
ValueCountFrequency (%)
1866
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 415
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31160
93.2%
Common 2281
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4073
13.1%
E 3719
11.9%
A 3439
11.0%
T 3163
10.2%
O 2700
8.7%
W 1859
 
6.0%
S 1807
 
5.8%
R 1756
 
5.6%
H 1292
 
4.1%
U 1286
 
4.1%
Other values (9) 6066
19.5%
Common
ValueCountFrequency (%)
1866
81.8%
/ 415
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4073
12.2%
E 3719
11.1%
A 3439
10.3%
T 3163
9.5%
O 2700
 
8.1%
1866
 
5.6%
W 1859
 
5.6%
S 1807
 
5.4%
R 1756
 
5.3%
H 1292
 
3.9%
Other values (11) 7767
23.2%

numberofbuildings
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0738275
Minimum0
Maximum27
Zeros92
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:13.797545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93200851
Coefficient of variation (CV)0.86793128
Kurtosis331.84503
Mean1.0738275
Median Absolute Deviation (MAD)0
Skewness15.609197
Sum3549
Variance0.86863986
MonotonicityNot monotonic
2023-08-03T23:01:13.956265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3116
94.3%
0 92
 
2.8%
2 35
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.2%
8 3
 
0.1%
14 2
 
0.1%
9 2
 
0.1%
Other values (6) 7
 
0.2%
ValueCountFrequency (%)
0 92
 
2.8%
1 3116
94.3%
2 35
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.2%
7 1
 
< 0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
0.1%
11 1
 
< 0.1%
10 2
 
0.1%
9 2
 
0.1%
8 3
0.1%
7 1
 
< 0.1%
6 5
0.2%

numberoffloors
Real number (ℝ)

Distinct50
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7113464
Minimum0
Maximum99
Zeros15
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:14.162418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.4838146
Coefficient of variation (CV)1.1639591
Kurtosis57.020294
Mean4.7113464
Median Absolute Deviation (MAD)2
Skewness5.9725175
Sum15571
Variance30.072223
MonotonicityNot monotonic
2023-08-03T23:01:14.501097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 676
20.5%
3 676
20.5%
1 455
13.8%
2 427
12.9%
6 302
9.1%
5 293
8.9%
7 145
 
4.4%
8 63
 
1.9%
10 32
 
1.0%
11 32
 
1.0%
Other values (40) 204
 
6.2%
ValueCountFrequency (%)
0 15
 
0.5%
1 455
13.8%
2 427
12.9%
3 676
20.5%
4 676
20.5%
5 293
8.9%
6 302
9.1%
7 145
 
4.4%
8 63
 
1.9%
9 18
 
0.5%
ValueCountFrequency (%)
99 1
 
< 0.1%
76 1
 
< 0.1%
63 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
46 1
 
< 0.1%
42 6
0.2%
41 2
 
0.1%

propertygfatotal
Real number (ℝ)

HIGH CORRELATION 

Distinct3132
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91949.278
Minimum11285
Maximum2200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:14.717464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21295.2
Q128425
median44100
Q390959
95-th percentile319351.8
Maximum2200000
Range2188715
Interquartile range (IQR)62534

Descriptive statistics

Standard deviation149802.09
Coefficient of variation (CV)1.6291818
Kurtosis50.645139
Mean91949.278
Median Absolute Deviation (MAD)19733
Skewness5.9540224
Sum3.0389236 × 108
Variance2.2440666 × 1010
MonotonicityNot monotonic
2023-08-03T23:01:14.910326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
28800 7
 
0.2%
21600 7
 
0.2%
24000 6
 
0.2%
30720 4
 
0.1%
22320 4
 
0.1%
30240 4
 
0.1%
22344 3
 
0.1%
90000 3
 
0.1%
Other values (3122) 3250
98.3%
ValueCountFrequency (%)
11285 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
13157 1
< 0.1%
13661 1
< 0.1%
14101 1
< 0.1%
15398 1
< 0.1%
16000 1
< 0.1%
ValueCountFrequency (%)
2200000 1
< 0.1%
1952220 1
< 0.1%
1765970 1
< 0.1%
1605578 1
< 0.1%
1592914 1
< 0.1%
1585960 1
< 0.1%
1536606 1
< 0.1%
1400000 1
< 0.1%
1380959 1
< 0.1%
1354987 1
< 0.1%

propertygfaparking
Real number (ℝ)

ZEROS 

Distinct486
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8025.9144
Minimum0
Maximum512608
Zeros2811
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:15.104845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46002.2
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32511.754
Coefficient of variation (CV)4.0508474
Kurtosis58.793266
Mean8025.9144
Median Absolute Deviation (MAD)0
Skewness6.6534824
Sum26525647
Variance1.0570142 × 109
MonotonicityNot monotonic
2023-08-03T23:01:15.323257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2811
85.1%
13320 3
 
0.1%
22000 2
 
0.1%
12960 2
 
0.1%
25800 2
 
0.1%
30000 2
 
0.1%
100176 2
 
0.1%
10800 2
 
0.1%
20416 2
 
0.1%
15576 1
 
< 0.1%
Other values (476) 476
 
14.4%
ValueCountFrequency (%)
0 2811
85.1%
38 1
 
< 0.1%
260 1
 
< 0.1%
415 1
 
< 0.1%
604 1
 
< 0.1%
756 1
 
< 0.1%
800 1
 
< 0.1%
919 1
 
< 0.1%
1263 1
 
< 0.1%
1392 1
 
< 0.1%
ValueCountFrequency (%)
512608 1
< 0.1%
407795 1
< 0.1%
389860 1
< 0.1%
368980 1
< 0.1%
335109 1
< 0.1%
327680 1
< 0.1%
319400 1
< 0.1%
303707 1
< 0.1%
285688 1
< 0.1%
285000 1
< 0.1%

propertygfabuilding_s
Real number (ℝ)

HIGH CORRELATION 

Distinct3129
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83923.363
Minimum3636
Maximum2200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:15.531599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21024.8
Q127680
median43162
Q384385
95-th percentile281403.6
Maximum2200000
Range2196364
Interquartile range (IQR)56705

Descriptive statistics

Standard deviation132993.88
Coefficient of variation (CV)1.5847063
Kurtosis58.306551
Mean83923.363
Median Absolute Deviation (MAD)18938
Skewness6.2735082
Sum2.7736672 × 108
Variance1.7687373 × 1010
MonotonicityNot monotonic
2023-08-03T23:01:15.736662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
30240 4
 
0.1%
30720 4
 
0.1%
22320 4
 
0.1%
20000 3
 
0.1%
25380 3
 
0.1%
Other values (3119) 3250
98.3%
ValueCountFrequency (%)
3636 1
< 0.1%
10925 1
< 0.1%
11285 1
< 0.1%
11440 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
12806 1
< 0.1%
13157 1
< 0.1%
ValueCountFrequency (%)
2200000 1
< 0.1%
1765970 1
< 0.1%
1632820 1
< 0.1%
1592914 1
< 0.1%
1380959 1
< 0.1%
1323055 1
< 0.1%
1258280 1
< 0.1%
1215718 1
< 0.1%
1195387 1
< 0.1%
1172127 1
< 0.1%
Distinct461
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:16.076153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length255
Median length162
Mean length26.048109
Min length5

Characters and Unicode

Total characters86089
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique309 ?
Unique (%)9.3%

Sample

1st rowHotel
2nd rowHotel, Parking, Restaurant
3rd rowHotel
4th rowHotel
5th rowHotel, Parking, Swimming Pool
ValueCountFrequency (%)
multifamily 1687
17.3%
housing 1687
17.3%
parking 1073
11.0%
office 947
9.7%
store 466
 
4.8%
other 413
 
4.2%
retail 398
 
4.1%
warehouse 277
 
2.8%
non-refrigerated 260
 
2.7%
179
 
1.8%
Other values (96) 2362
24.2%
2023-08-03T23:01:16.670017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9245
 
10.7%
6444
 
7.5%
e 5620
 
6.5%
a 5186
 
6.0%
t 4880
 
5.7%
l 4877
 
5.7%
u 4213
 
4.9%
r 4199
 
4.9%
n 4123
 
4.8%
o 3936
 
4.6%
Other values (42) 33366
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65473
76.1%
Uppercase Letter 10200
 
11.8%
Space Separator 6444
 
7.5%
Other Punctuation 3020
 
3.5%
Dash Punctuation 614
 
0.7%
Decimal Number 262
 
0.3%
Open Punctuation 38
 
< 0.1%
Close Punctuation 38
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9245
14.1%
e 5620
 
8.6%
a 5186
 
7.9%
t 4880
 
7.5%
l 4877
 
7.4%
u 4213
 
6.4%
r 4199
 
6.4%
n 4123
 
6.3%
o 3936
 
6.0%
f 3924
 
6.0%
Other values (12) 15270
23.3%
Uppercase Letter
ValueCountFrequency (%)
H 1887
18.5%
M 1836
18.0%
O 1382
13.5%
P 1221
12.0%
S 1007
9.9%
R 952
9.3%
W 351
 
3.4%
C 335
 
3.3%
N 266
 
2.6%
F 219
 
2.1%
Other values (11) 744
 
7.3%
Other Punctuation
ValueCountFrequency (%)
, 2650
87.7%
/ 358
 
11.9%
& 12
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 131
50.0%
2 131
50.0%
Space Separator
ValueCountFrequency (%)
6444
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 614
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75673
87.9%
Common 10416
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9245
 
12.2%
e 5620
 
7.4%
a 5186
 
6.9%
t 4880
 
6.4%
l 4877
 
6.4%
u 4213
 
5.6%
r 4199
 
5.5%
n 4123
 
5.4%
o 3936
 
5.2%
f 3924
 
5.2%
Other values (33) 25470
33.7%
Common
ValueCountFrequency (%)
6444
61.9%
, 2650
25.4%
- 614
 
5.9%
/ 358
 
3.4%
1 131
 
1.3%
2 131
 
1.3%
( 38
 
0.4%
) 38
 
0.4%
& 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9245
 
10.7%
6444
 
7.5%
e 5620
 
6.5%
a 5186
 
6.0%
t 4880
 
5.7%
l 4877
 
5.7%
u 4213
 
4.9%
r 4199
 
4.9%
n 4123
 
4.8%
o 3936
 
4.6%
Other values (42) 33366
38.8%
Distinct55
Distinct (%)1.7%
Missing11
Missing (%)0.3%
Memory size25.9 KiB
2023-08-03T23:01:17.168756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length52
Median length19
Mean length16.317851
Min length5

Characters and Unicode

Total characters53751
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
multifamily 1648
27.3%
housing 1648
27.3%
office 532
 
8.8%
warehouse 211
 
3.5%
non-refrigerated 199
 
3.3%
other 174
 
2.9%
store 138
 
2.3%
k-12 123
 
2.0%
school 123
 
2.0%
retail 97
 
1.6%
Other values (78) 1153
19.1%
2023-08-03T23:01:17.771126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6735
 
12.5%
l 4063
 
7.6%
u 3755
 
7.0%
t 3056
 
5.7%
o 3007
 
5.6%
e 2998
 
5.6%
f 2964
 
5.5%
a 2774
 
5.2%
2752
 
5.1%
n 2364
 
4.4%
Other values (41) 19283
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43755
81.4%
Uppercase Letter 6343
 
11.8%
Space Separator 2752
 
5.1%
Dash Punctuation 428
 
0.8%
Decimal Number 246
 
0.5%
Other Punctuation 193
 
0.4%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6735
15.4%
l 4063
9.3%
u 3755
 
8.6%
t 3056
 
7.0%
o 3007
 
6.9%
e 2998
 
6.9%
f 2964
 
6.8%
a 2774
 
6.3%
n 2364
 
5.4%
s 2164
 
4.9%
Other values (11) 9875
22.6%
Uppercase Letter
ValueCountFrequency (%)
H 1778
28.0%
M 1733
27.3%
O 718
11.3%
S 458
 
7.2%
R 390
 
6.1%
W 279
 
4.4%
N 199
 
3.1%
C 195
 
3.1%
K 123
 
1.9%
F 107
 
1.7%
Other values (11) 363
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/ 163
84.5%
, 20
 
10.4%
& 10
 
5.2%
Decimal Number
ValueCountFrequency (%)
2 123
50.0%
1 123
50.0%
Space Separator
ValueCountFrequency (%)
2752
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 428
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50098
93.2%
Common 3653
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6735
13.4%
l 4063
 
8.1%
u 3755
 
7.5%
t 3056
 
6.1%
o 3007
 
6.0%
e 2998
 
6.0%
f 2964
 
5.9%
a 2774
 
5.5%
n 2364
 
4.7%
s 2164
 
4.3%
Other values (32) 16218
32.4%
Common
ValueCountFrequency (%)
2752
75.3%
- 428
 
11.7%
/ 163
 
4.5%
2 123
 
3.4%
1 123
 
3.4%
, 20
 
0.5%
( 17
 
0.5%
) 17
 
0.5%
& 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6735
 
12.5%
l 4063
 
7.6%
u 3755
 
7.0%
t 3056
 
5.7%
o 3007
 
5.6%
e 2998
 
5.6%
f 2964
 
5.5%
a 2774
 
5.2%
2752
 
5.1%
n 2364
 
4.4%
Other values (41) 19283
35.9%

largestpropertyusetypegfa
Real number (ℝ)

HIGH CORRELATION 

Distinct3068
Distinct (%)93.1%
Missing11
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean76167.648
Minimum5656
Maximum1719643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:17.979738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5656
5-th percentile17509.1
Q125097
median39828
Q375595
95-th percentile242873.05
Maximum1719643
Range1713987
Interquartile range (IQR)50498

Descriptive statistics

Standard deviation122950.07
Coefficient of variation (CV)1.6142033
Kurtosis55.898439
Mean76167.648
Median Absolute Deviation (MAD)17485.5
Skewness6.2758764
Sum2.5089623 × 108
Variance1.511672 × 1010
MonotonicityNot monotonic
2023-08-03T23:01:18.213549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000 9
 
0.3%
22000 8
 
0.2%
30000 7
 
0.2%
21600 7
 
0.2%
20000 7
 
0.2%
24288 5
 
0.2%
15000 5
 
0.2%
36000 5
 
0.2%
28800 5
 
0.2%
45000 5
 
0.2%
Other values (3058) 3231
97.8%
(Missing) 11
 
0.3%
ValueCountFrequency (%)
5656 1
< 0.1%
6455 1
< 0.1%
6601 1
< 0.1%
6900 1
< 0.1%
7245 1
< 0.1%
7387 1
< 0.1%
7501 1
< 0.1%
7583 1
< 0.1%
7758 1
< 0.1%
8061 1
< 0.1%
ValueCountFrequency (%)
1719643 1
< 0.1%
1680937 1
< 0.1%
1639334 1
< 0.1%
1585960 1
< 0.1%
1350182 1
< 0.1%
1314475 1
< 0.1%
1191115 1
< 0.1%
1172127 1
< 0.1%
1011135 1
< 0.1%
1010135 1
< 0.1%

energystarscore
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)4.0%
Missing805
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean67.7996
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:18.433902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q153
median75
Q390
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.73291
Coefficient of variation (CV)0.39429304
Kurtosis-0.22628938
Mean67.7996
Median Absolute Deviation (MAD)17
Skewness-0.85454738
Sum169499
Variance714.6485
MonotonicityNot monotonic
2023-08-03T23:01:18.641934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 92
 
2.8%
98 72
 
2.2%
96 64
 
1.9%
89 58
 
1.8%
93 56
 
1.7%
92 53
 
1.6%
95 51
 
1.5%
94 49
 
1.5%
91 49
 
1.5%
99 48
 
1.5%
Other values (90) 1908
57.7%
(Missing) 805
24.4%
ValueCountFrequency (%)
1 33
1.0%
2 10
 
0.3%
3 13
 
0.4%
4 5
 
0.2%
5 8
 
0.2%
6 8
 
0.2%
7 10
 
0.3%
8 10
 
0.3%
9 5
 
0.2%
10 10
 
0.3%
ValueCountFrequency (%)
100 92
2.8%
99 48
1.5%
98 72
2.2%
97 48
1.5%
96 64
1.9%
95 51
1.5%
94 49
1.5%
93 56
1.7%
92 53
1.6%
91 49
1.5%

siteeui_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1064
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.937761
Minimum1.4
Maximum834.40002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:18.848775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile18.799999
Q128.200001
median38.799999
Q360.400002
95-th percentile144.5
Maximum834.40002
Range833.00002
Interquartile range (IQR)32.200001

Descriptive statistics

Standard deviation55.91784
Coefficient of variation (CV)1.0178398
Kurtosis41.5498
Mean54.937761
Median Absolute Deviation (MAD)13.4
Skewness5.0936536
Sum181569.3
Variance3126.8048
MonotonicityNot monotonic
2023-08-03T23:01:19.040117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.79999924 17
 
0.5%
24.70000076 17
 
0.5%
24.20000076 16
 
0.5%
32 15
 
0.5%
28.89999962 14
 
0.4%
26.39999962 14
 
0.4%
31.70000076 14
 
0.4%
26.60000038 13
 
0.4%
30.60000038 13
 
0.4%
29.60000038 13
 
0.4%
Other values (1054) 3159
95.6%
ValueCountFrequency (%)
1.399999976 1
< 0.1%
2.099999905 1
< 0.1%
2.299999952 1
< 0.1%
3 1
< 0.1%
3.200000048 1
< 0.1%
3.5 2
0.1%
3.599999905 2
0.1%
3.799999952 1
< 0.1%
4.300000191 1
< 0.1%
4.400000095 1
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
696.7000122 1
< 0.1%
694.7000122 1
< 0.1%
639.7000122 1
< 0.1%
593.5999756 1
< 0.1%
465.5 1
< 0.1%
456.6000061 1
< 0.1%
438.2000122 1
< 0.1%
412.7000122 1
< 0.1%

siteeuiwn_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1085
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.559032
Minimum1.5
Maximum834.40002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:19.376055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile19.9
Q129.799999
median41.299999
Q364.5
95-th percentile148.88
Maximum834.40002
Range832.90002
Interquartile range (IQR)34.700001

Descriptive statistics

Standard deviation56.813729
Coefficient of variation (CV)0.98705151
Kurtosis39.016442
Mean57.559032
Median Absolute Deviation (MAD)14.199999
Skewness4.9354793
Sum190232.6
Variance3227.7998
MonotonicityNot monotonic
2023-08-03T23:01:19.564577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.5 17
 
0.5%
30.79999924 15
 
0.5%
31.60000038 14
 
0.4%
29 14
 
0.4%
27.89999962 14
 
0.4%
32.20000076 14
 
0.4%
30.20000076 14
 
0.4%
31.39999962 13
 
0.4%
33.59999847 13
 
0.4%
28.10000038 13
 
0.4%
Other values (1075) 3164
95.7%
ValueCountFrequency (%)
1.5 1
< 0.1%
2.099999905 1
< 0.1%
2.299999952 1
< 0.1%
3 1
< 0.1%
3.200000048 1
< 0.1%
3.5 1
< 0.1%
3.599999905 2
0.1%
4 1
< 0.1%
4.300000191 2
0.1%
4.599999905 1
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
694.7000122 1
< 0.1%
693.0999756 1
< 0.1%
639.7999878 1
< 0.1%
593.5999756 1
< 0.1%
468.7000122 1
< 0.1%
467 1
< 0.1%
460.1000061 1
< 0.1%
426.6000061 1
< 0.1%

sourceeui_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1620
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.72669
Minimum0
Maximum2620
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:19.756274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.760001
Q175.300003
median96.599998
Q3143.89999
95-th percentile350.03999
Maximum2620
Range2620
Interquartile range (IQR)68.599991

Descriptive statistics

Standard deviation137.7924
Coefficient of variation (CV)1.022755
Kurtosis81.860617
Mean134.72669
Median Absolute Deviation (MAD)27.400002
Skewness6.7843796
Sum445271.7
Variance18986.745
MonotonicityNot monotonic
2023-08-03T23:01:19.960025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.09999847 9
 
0.3%
83.69999695 9
 
0.3%
90.5 8
 
0.2%
73.09999847 8
 
0.2%
94.09999847 8
 
0.2%
69.69999695 8
 
0.2%
78.59999847 8
 
0.2%
87.69999695 8
 
0.2%
91.5 7
 
0.2%
98 7
 
0.2%
Other values (1610) 3225
97.6%
ValueCountFrequency (%)
0 6
0.2%
2 1
 
< 0.1%
4.5 1
 
< 0.1%
6.599999905 2
 
0.1%
6.900000095 1
 
< 0.1%
9 1
 
< 0.1%
9.5 1
 
< 0.1%
9.899999619 1
 
< 0.1%
10.19999981 1
 
< 0.1%
11.10000038 1
 
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2007.900024 1
< 0.1%
1527.300049 1
< 0.1%
1206.699951 1
< 0.1%
1150.300049 1
< 0.1%
1026.599976 1
< 0.1%
962.0999756 1
< 0.1%
912.7999878 1
< 0.1%

sourceeuiwn_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1670
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.99195
Minimum-2.0999999
Maximum2620
Zeros6
Zeros (%)0.2%
Negative1
Negative (%)< 0.1%
Memory size25.9 KiB
2023-08-03T23:01:20.162481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.0999999
5-th percentile46.940001
Q179.300003
median101.9
Q3149.10001
95-th percentile351.16001
Maximum2620
Range2622.1
Interquartile range (IQR)69.800003

Descriptive statistics

Standard deviation137.54754
Coefficient of variation (CV)0.98960796
Kurtosis81.664111
Mean138.99195
Median Absolute Deviation (MAD)28.300003
Skewness6.7633706
Sum459368.4
Variance18919.326
MonotonicityNot monotonic
2023-08-03T23:01:20.564822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.30000305 9
 
0.3%
73.59999847 9
 
0.3%
93.59999847 8
 
0.2%
75.5 8
 
0.2%
83.5 8
 
0.2%
104.5999985 8
 
0.2%
102.4000015 8
 
0.2%
84.90000153 8
 
0.2%
98.90000153 8
 
0.2%
93.40000153 7
 
0.2%
Other values (1660) 3224
97.5%
ValueCountFrequency (%)
-2.099999905 1
 
< 0.1%
0 6
0.2%
4.599999905 1
 
< 0.1%
6.599999905 1
 
< 0.1%
6.900000095 1
 
< 0.1%
7.400000095 1
 
< 0.1%
9 1
 
< 0.1%
9.5 1
 
< 0.1%
10 1
 
< 0.1%
10.30000019 1
 
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2008 1
< 0.1%
1527.300049 1
< 0.1%
1195.099976 1
< 0.1%
1138.400024 1
< 0.1%
1001 1
< 0.1%
954 1
< 0.1%
919.2999878 1
< 0.1%

siteenergyuse_kbtu
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3305
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5159151.5
Minimum57133.199
Maximum4.4838531 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:20.968582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57133.199
5-th percentile520818.77
Q1942415.19
median1815371.2
Q34217623
95-th percentile17843800
Maximum4.4838531 × 108
Range4.4832818 × 108
Interquartile range (IQR)3275207.8

Descriptive statistics

Standard deviation15686664
Coefficient of variation (CV)3.0405512
Kurtosis314.23587
Mean5159151.5
Median Absolute Deviation (MAD)1067376.4
Skewness14.883606
Sum1.7050996 × 1010
Variance2.4607143 × 1014
MonotonicityNot monotonic
2023-08-03T23:01:21.313862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7226362.5 1
 
< 0.1%
8163413 1
 
< 0.1%
488991.5 1
 
< 0.1%
1206165.75 1
 
< 0.1%
1302192.875 1
 
< 0.1%
150167.7969 1
 
< 0.1%
1386445.375 1
 
< 0.1%
1331469.75 1
 
< 0.1%
421389.4063 1
 
< 0.1%
12213423 1
 
< 0.1%
Other values (3295) 3295
99.7%
ValueCountFrequency (%)
57133.19922 1
< 0.1%
79711.79688 1
< 0.1%
90558.70313 1
< 0.1%
97690.39844 1
< 0.1%
106918 1
< 0.1%
111969.7031 1
< 0.1%
113130 1
< 0.1%
116486.6016 1
< 0.1%
117438.3984 1
< 0.1%
123767.2031 1
< 0.1%
ValueCountFrequency (%)
448385312 1
< 0.1%
293090784 1
< 0.1%
291614432 1
< 0.1%
274682208 1
< 0.1%
253832464 1
< 0.1%
163945984 1
< 0.1%
143423024 1
< 0.1%
131373880 1
< 0.1%
114648520 1
< 0.1%
102673696 1
< 0.1%

siteenergyusewn_kbtu
Real number (ℝ)

HIGH CORRELATION 

Distinct3304
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5337462.7
Minimum58114.199
Maximum4.7161386 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:21.686739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum58114.199
5-th percentile549895.8
Q1997020.5
median1943594.9
Q34419299.5
95-th percentile18422123
Maximum4.7161386 × 108
Range4.7155574 × 108
Interquartile range (IQR)3422279

Descriptive statistics

Standard deviation16062679
Coefficient of variation (CV)3.0094221
Kurtosis330.42155
Mean5337462.7
Median Absolute Deviation (MAD)1139084.5
Skewness15.196966
Sum1.7640314 × 1010
Variance2.5800964 × 1014
MonotonicityNot monotonic
2023-08-03T23:01:22.086944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2127889.25 2
 
0.1%
7456910 1
 
< 0.1%
4653535 1
 
< 0.1%
909471.875 1
 
< 0.1%
509741.1875 1
 
< 0.1%
1355995.25 1
 
< 0.1%
1439042.25 1
 
< 0.1%
150167.7969 1
 
< 0.1%
1386445.375 1
 
< 0.1%
1519845.5 1
 
< 0.1%
Other values (3294) 3294
99.7%
ValueCountFrequency (%)
58114.19922 1
< 0.1%
79967.89844 1
< 0.1%
90558.70313 1
< 0.1%
98862.89844 1
< 0.1%
109471.7969 1
< 0.1%
116486.6016 1
< 0.1%
116642.5 1
< 0.1%
120610.5 1
< 0.1%
127374 1
< 0.1%
128383.8984 1
< 0.1%
ValueCountFrequency (%)
471613856 1
< 0.1%
296671744 1
< 0.1%
295929888 1
< 0.1%
274725984 1
< 0.1%
257764208 1
< 0.1%
167207104 1
< 0.1%
147299056 1
< 0.1%
137106112 1
< 0.1%
123205560 1
< 0.1%
103985264 1
< 0.1%

steam
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3179 
True
 
126
ValueCountFrequency (%)
False 3179
96.2%
True 126
 
3.8%
2023-08-03T23:01:22.447255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

electricity
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
True
3301 
False
 
4
ValueCountFrequency (%)
True 3301
99.9%
False 4
 
0.1%
2023-08-03T23:01:22.760141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

naturalgas
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
True
2084 
False
1221 
ValueCountFrequency (%)
True 2084
63.1%
False 1221
36.9%
2023-08-03T23:01:23.076859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

defaultdata
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3195 
True
 
110
ValueCountFrequency (%)
False 3195
96.7%
True 110
 
3.3%
2023-08-03T23:01:23.394567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

compliancestatus
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
Compliant
3195 
Error - Correct Default Data
 
110

Length

Max length28
Median length9
Mean length9.6323752
Min length9

Characters and Unicode

Total characters31835
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompliant
2nd rowCompliant
3rd rowCompliant
4th rowCompliant
5th rowCompliant

Common Values

ValueCountFrequency (%)
Compliant 3195
96.7%
Error - Correct Default Data 110
 
3.3%

Length

2023-08-03T23:01:23.712643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T23:01:24.083643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
compliant 3195
85.3%
error 110
 
2.9%
110
 
2.9%
correct 110
 
2.9%
default 110
 
2.9%
data 110
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 3525
11.1%
t 3525
11.1%
o 3415
10.7%
C 3305
10.4%
l 3305
10.4%
m 3195
10.0%
p 3195
10.0%
i 3195
10.0%
n 3195
10.0%
r 550
 
1.7%
Other values (8) 1430
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27650
86.9%
Uppercase Letter 3635
 
11.4%
Space Separator 440
 
1.4%
Dash Punctuation 110
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3525
12.7%
t 3525
12.7%
o 3415
12.4%
l 3305
12.0%
m 3195
11.6%
p 3195
11.6%
i 3195
11.6%
n 3195
11.6%
r 550
 
2.0%
e 220
 
0.8%
Other values (3) 330
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
C 3305
90.9%
D 220
 
6.1%
E 110
 
3.0%
Space Separator
ValueCountFrequency (%)
440
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31285
98.3%
Common 550
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3525
11.3%
t 3525
11.3%
o 3415
10.9%
C 3305
10.6%
l 3305
10.6%
m 3195
10.2%
p 3195
10.2%
i 3195
10.2%
n 3195
10.2%
r 550
 
1.8%
Other values (6) 880
 
2.8%
Common
ValueCountFrequency (%)
440
80.0%
- 110
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3525
11.1%
t 3525
11.1%
o 3415
10.7%
C 3305
10.4%
l 3305
10.4%
m 3195
10.0%
p 3195
10.0%
i 3195
10.0%
n 3195
10.0%
r 550
 
1.7%
Other values (8) 1430
4.5%

totalghgemissions
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2774
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.79804
Minimum-0.8
Maximum16870.98
Zeros2
Zeros (%)0.1%
Negative1
Negative (%)< 0.1%
Memory size25.9 KiB
2023-08-03T23:01:24.399584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile3.92
Q19.66
median34.15
Q393.68
95-th percentile391.494
Maximum16870.98
Range16871.78
Interquartile range (IQR)84.02

Descriptive statistics

Standard deviation508.31012
Coefficient of variation (CV)4.3520434
Kurtosis545.24634
Mean116.79804
Median Absolute Deviation (MAD)28.02
Skewness20.570601
Sum386017.51
Variance258379.18
MonotonicityNot monotonic
2023-08-03T23:01:24.792305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.95 7
 
0.2%
4.2 6
 
0.2%
5.46 5
 
0.2%
9.29 5
 
0.2%
4.02 5
 
0.2%
4.43 5
 
0.2%
5.07 5
 
0.2%
3.54 5
 
0.2%
4.52 5
 
0.2%
6.41 5
 
0.2%
Other values (2764) 3252
98.4%
ValueCountFrequency (%)
-0.8 1
< 0.1%
0 2
0.1%
0.4 1
< 0.1%
0.63 1
< 0.1%
0.68 1
< 0.1%
0.75 1
< 0.1%
0.79 1
< 0.1%
0.81 1
< 0.1%
0.82 1
< 0.1%
0.86 1
< 0.1%
ValueCountFrequency (%)
16870.98 1
< 0.1%
12307.16 1
< 0.1%
10734.57 1
< 0.1%
8145.52 1
< 0.1%
6330.91 1
< 0.1%
4906.33 1
< 0.1%
3995.45 1
< 0.1%
3768.66 1
< 0.1%
3278.11 1
< 0.1%
3243.48 1
< 0.1%

zipcode
Real number (ℝ)

Distinct60
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98116.934
Minimum98006
Maximum98272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:25.154846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98006
5-th percentile98101
Q198105
median98115
Q398122
95-th percentile98144
Maximum98272
Range266
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.551232
Coefficient of variation (CV)0.00018907268
Kurtosis10.601832
Mean98116.934
Median Absolute Deviation (MAD)10
Skewness1.9851626
Sum3.2427647 × 108
Variance344.1482
MonotonicityNot monotonic
2023-08-03T23:01:25.677309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98109 291
 
8.8%
98104 245
 
7.4%
98122 238
 
7.2%
98101 222
 
6.7%
98105 185
 
5.6%
98134 184
 
5.6%
98121 183
 
5.5%
98102 167
 
5.1%
98119 164
 
5.0%
98103 159
 
4.8%
Other values (50) 1267
38.3%
ValueCountFrequency (%)
98006 1
< 0.1%
98011 1
< 0.1%
98012 1
< 0.1%
98013 2
0.1%
98020 1
< 0.1%
98028 1
< 0.1%
98033 1
< 0.1%
98040 1
< 0.1%
98053 1
< 0.1%
98070 1
< 0.1%
ValueCountFrequency (%)
98272 1
 
< 0.1%
98204 1
 
< 0.1%
98199 69
2.1%
98198 1
 
< 0.1%
98195 8
 
0.2%
98191 1
 
< 0.1%
98185 1
 
< 0.1%
98181 1
 
< 0.1%
98178 4
 
0.1%
98177 2
 
0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2823
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.624094
Minimum47.49917
Maximum47.73387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:26.059630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.49917
5-th percentile47.541566
Q147.60001
median47.61876
Q347.65718
95-th percentile47.713044
Maximum47.73387
Range0.2347
Interquartile range (IQR)0.05717

Descriptive statistics

Standard deviation0.0477716
Coefficient of variation (CV)0.0010030973
Kurtosis-0.14174012
Mean47.624094
Median Absolute Deviation (MAD)0.02835
Skewness0.13879301
Sum157397.63
Variance0.0022821258
MonotonicityNot monotonic
2023-08-03T23:01:26.460114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.66246 9
 
0.3%
47.61598 7
 
0.2%
47.62208 6
 
0.2%
47.61543 5
 
0.2%
47.52549 5
 
0.2%
47.62395 5
 
0.2%
47.61048 4
 
0.1%
47.5829 4
 
0.1%
47.60071 4
 
0.1%
47.6239 4
 
0.1%
Other values (2813) 3252
98.4%
ValueCountFrequency (%)
47.49917 1
< 0.1%
47.50061895 1
< 0.1%
47.50224 1
< 0.1%
47.50959 1
< 0.1%
47.51018 1
< 0.1%
47.51042 1
< 0.1%
47.51098 1
< 0.1%
47.51104 1
< 0.1%
47.51127 2
0.1%
47.51138 1
< 0.1%
ValueCountFrequency (%)
47.73387 1
< 0.1%
47.73375 1
< 0.1%
47.73368 1
< 0.1%
47.7336 1
< 0.1%
47.73357 1
< 0.1%
47.73351 1
< 0.1%
47.73331 1
< 0.1%
47.73316 1
< 0.1%
47.73315 1
< 0.1%
47.73279 1
< 0.1%

longitude
Real number (ℝ)

Distinct2609
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.33474
Minimum-122.41425
Maximum-122.22097
Zeros0
Zeros (%)0.0%
Negative3305
Negative (%)100.0%
Memory size25.9 KiB
2023-08-03T23:01:26.840905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.3865
Q1-122.35046
median-122.33248
Q3-122.3195
95-th percentile-122.28925
Maximum-122.22097
Range0.1932841
Interquartile range (IQR)0.03096

Descriptive statistics

Standard deviation0.027141532
Coefficient of variation (CV)-0.00022186284
Kurtosis0.27817438
Mean-122.33474
Median Absolute Deviation (MAD)0.01499
Skewness-0.13321834
Sum-404316.31
Variance0.00073666278
MonotonicityNot monotonic
2023-08-03T23:01:27.101111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29898 8
 
0.2%
-122.35398 7
 
0.2%
-122.32468 6
 
0.2%
-122.33369 6
 
0.2%
-122.33379 5
 
0.2%
-122.33064 5
 
0.2%
-122.32417 5
 
0.2%
-122.31769 5
 
0.2%
-122.32592 5
 
0.2%
-122.32811 4
 
0.1%
Other values (2599) 3249
98.3%
ValueCountFrequency (%)
-122.41425 1
< 0.1%
-122.41182 1
< 0.1%
-122.41178 1
< 0.1%
-122.41169 1
< 0.1%
-122.41037 1
< 0.1%
-122.41036 1
< 0.1%
-122.41031 1
< 0.1%
-122.40976 1
< 0.1%
-122.40974 1
< 0.1%
-122.40901 1
< 0.1%
ValueCountFrequency (%)
-122.2209659 1
< 0.1%
-122.25864 1
< 0.1%
-122.26028 1
< 0.1%
-122.26034 1
< 0.1%
-122.26166 2
0.1%
-122.26172 1
< 0.1%
-122.26177 1
< 0.1%
-122.2618 1
< 0.1%
-122.26216 1
< 0.1%
-122.26223 1
< 0.1%

age
Real number (ℝ)

Distinct113
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.277458
Minimum8
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-08-03T23:01:27.318100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q126
median48
Q375
95-th percentile115
Maximum123
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.045765
Coefficient of variation (CV)0.60883036
Kurtosis-0.86800233
Mean54.277458
Median Absolute Deviation (MAD)24
Skewness0.54242952
Sum179387
Variance1092.0226
MonotonicityNot monotonic
2023-08-03T23:01:27.617788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 70
 
2.1%
9 66
 
2.0%
15 65
 
2.0%
24 63
 
1.9%
55 63
 
1.9%
34 63
 
1.9%
35 60
 
1.8%
22 59
 
1.8%
33 58
 
1.8%
21 58
 
1.8%
Other values (103) 2680
81.1%
ValueCountFrequency (%)
8 34
1.0%
9 66
2.0%
10 51
1.5%
11 35
1.1%
12 15
 
0.5%
13 24
 
0.7%
14 41
1.2%
15 65
2.0%
16 42
1.3%
17 45
1.4%
ValueCountFrequency (%)
123 52
1.6%
122 8
 
0.2%
121 11
 
0.3%
120 3
 
0.1%
119 14
 
0.4%
118 9
 
0.3%
117 18
 
0.5%
116 31
0.9%
115 27
0.8%
114 31
0.9%

source_site
Real number (ℝ)

HIGH CORRELATION 

Distinct2950
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5558499
Minimum-0.41999998
Maximum13.211111
Zeros6
Zeros (%)0.2%
Negative1
Negative (%)< 0.1%
Memory size25.9 KiB
2023-08-03T23:01:27.851687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.41999998
5-th percentile1.4618124
Q12.115583
median2.634804
Q33.1384615
95-th percentile3.1439394
Maximum13.211111
Range13.631111
Interquartile range (IQR)1.0228784

Descriptive statistics

Standard deviation0.62889809
Coefficient of variation (CV)0.24606221
Kurtosis24.340422
Mean2.5558499
Median Absolute Deviation (MAD)0.50380992
Skewness0.92513742
Sum8447.084
Variance0.39551281
MonotonicityNot monotonic
2023-08-03T23:01:28.251525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.142857143 7
 
0.2%
0 6
 
0.2%
3.140939626 5
 
0.2%
3.137184085 5
 
0.2%
3.140077609 5
 
0.2%
3.136363636 5
 
0.2%
3.142276498 5
 
0.2%
3.13620076 5
 
0.2%
3.1390977 5
 
0.2%
3.144654163 5
 
0.2%
Other values (2940) 3252
98.4%
ValueCountFrequency (%)
-0.419999981 1
 
< 0.1%
0 6
0.2%
0.4214285895 1
 
< 0.1%
1.049797122 1
 
< 0.1%
1.050158691 1
 
< 0.1%
1.059027806 1
 
< 0.1%
1.082949271 1
 
< 0.1%
1.115999985 1
 
< 0.1%
1.130331751 1
 
< 0.1%
1.134883703 1
 
< 0.1%
ValueCountFrequency (%)
13.21111111 1
< 0.1%
5.204283195 1
< 0.1%
4.668176647 1
< 0.1%
4.585902864 1
< 0.1%
3.188596464 1
< 0.1%
3.173913192 1
< 0.1%
3.166666667 1
< 0.1%
3.166666645 1
< 0.1%
3.163636295 1
< 0.1%
3.157894843 1
< 0.1%

Interactions

2023-08-03T23:01:04.569436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:38.622328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:43.074944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:46.482654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:51.197318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:54.494384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:58.191546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:01.642254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:05.483083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:08.891080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:12.544952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:15.809897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:21.247975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:26.373812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:32.089113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:37.869778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:41.712294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:47.391129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:52.855444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:58.893040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:04.861114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:38.807377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:43.246186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:46.721807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:51.367818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:54.669874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:58.357642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:01.824249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:05.656948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:09.059469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:12.718028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:15.980633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:21.429332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:26.628022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:32.376297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:38.044136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:42.001915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:47.675931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:53.154942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:59.185083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:05.149523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:39.031590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:43.405497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:47.119427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:51.528388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:54.834518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:58.521807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:01.997146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:05.835859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:09.224261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:12.876413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:16.249199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:21.590971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:26.911541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:32.656571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:38.217538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:42.258616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:47.932951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:53.447884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:59.465066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:05.433067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:39.268916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:43.565985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:47.398080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:51.692209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:54.999523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:58.687630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:02.174124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:05.998159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:09.381945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:13.036063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:16.521308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:21.866517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:27.190868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:32.912995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:38.442716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:42.540055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:48.213891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:53.740560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:59.744991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:05.716798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:39.551516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:43.734493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:47.678838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:51.846601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:55.297697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:58.846708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:02.344453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:06.201921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:09.540795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:13.197618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:16.802646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:22.146404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:27.469413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:33.187862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:38.611026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:42.821057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:48.492470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:54.167423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:00.025231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:06.000738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:39.839356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:43.899710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:47.960938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:52.009825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:55.458162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:59.129814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:02.625902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:06.366423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:09.704471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:13.359065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:17.087403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:22.427299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:27.750800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:33.470531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-08-03T23:00:45.956733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:51.430781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:57.408461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:03.121196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:09.220651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:42.305258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:45.755087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:50.456496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:53.830828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:57.484223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:00.946153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:04.766083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:08.212103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:11.855644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:15.140506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:20.321061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:25.321394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:30.841152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:36.939332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:40.689932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:46.240392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:51.713732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:57.703474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:03.404973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:09.500257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:42.464537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:45.921711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:50.619771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:53.987108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:57.668099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:01.131014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:04.939226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:08.374135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:12.014930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:15.298386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:20.601354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:25.600871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:31.122790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:37.223476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:40.855237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:46.519305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:51.986944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:57.994033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:03.681435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:09.806935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:42.682351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:46.150759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:50.799568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:54.170944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:57.852096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:01.309067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:05.133238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:08.559065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:12.214318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:15.485043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:20.814481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:25.903779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:31.526453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:37.536619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:41.052674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:46.822623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:52.291571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:58.302402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:03.987343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:10.090334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:42.906050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:46.314997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:50.960000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:54.329275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T22:59:58.015078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:01.473217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:05.304851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:08.723439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:12.378206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:15.646066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:20.977690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:26.195336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:31.806016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:37.703713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:41.339661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:47.104596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:52.569332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:00:58.594857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T23:01:04.281494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-03T23:01:28.607393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
councildistrictcodenumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slargestpropertyusetypegfaenergystarscoresiteeui_kbtu_sfsiteeuiwn_kbtu_sfsourceeui_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtutotalghgemissionszipcodelatitudelongitudeagesource_sitebuildingtypeprimarypropertytypeneighborhoodsteamelectricitynaturalgasdefaultdatacompliancestatus
councildistrictcode1.000-0.0200.3340.1540.1520.1440.1260.0740.0920.0820.1100.1050.1470.1430.120-0.1930.514-0.350-0.0030.0130.1490.2510.8790.2150.0000.1390.0980.098
numberofbuildings-0.0201.000-0.0250.0640.0040.0630.0790.0360.0020.002-0.004-0.0040.0520.0530.0450.0160.0510.038-0.038-0.0100.1830.2010.0360.0000.0000.0000.0000.000
numberoffloors0.334-0.0251.0000.4430.2580.4350.4170.1290.010-0.0100.0860.0780.2800.2730.168-0.2270.068-0.115-0.2930.1300.2640.2780.1400.2960.0000.0440.0000.000
propertygfatotal0.1540.0640.4431.0000.3450.9830.9290.0830.1860.1700.2110.1980.7660.7630.581-0.091-0.056-0.022-0.315-0.0310.1260.1940.0660.2030.0000.0900.0000.000
propertygfaparking0.1520.0040.2580.3451.0000.2210.2720.0130.1950.1810.2430.2350.3020.2970.205-0.1250.018-0.050-0.2400.0630.0520.1540.0600.0830.0000.0100.0000.000
propertygfabuilding_s0.1440.0630.4350.9830.2211.0000.9280.0840.1620.1480.1800.1680.7520.7490.577-0.078-0.065-0.017-0.286-0.0420.1290.1920.0630.2300.0000.0880.0000.000
largestpropertyusetypegfa0.1260.0790.4170.9290.2720.9281.0000.0940.1210.1090.1280.1170.7320.7310.568-0.053-0.048-0.012-0.293-0.0430.1190.1970.0640.2250.0000.0940.0000.000
energystarscore0.0740.0360.1290.0830.0130.0840.0941.000-0.447-0.450-0.515-0.527-0.175-0.176-0.100-0.0030.085-0.034-0.082-0.0190.1190.1210.0560.0140.0000.1010.1110.111
siteeui_kbtu_sf0.0920.0020.0100.1860.1950.1620.121-0.4471.0000.9970.8670.8750.7020.7040.710-0.128-0.0820.0430.066-0.4030.1330.2790.0560.1220.0870.1530.0500.050
siteeuiwn_kbtu_sf0.0820.002-0.0100.1700.1810.1480.109-0.4500.9971.0000.8460.8580.6910.6950.716-0.126-0.0840.0450.088-0.4340.1380.2710.0550.1210.0830.1750.0570.057
sourceeui_kbtu_sf0.110-0.0040.0860.2110.2430.1800.128-0.5150.8670.8461.0000.9980.6290.6200.461-0.105-0.0510.027-0.0600.0220.1130.2450.0270.0390.0000.0700.0250.025
sourceeuiwn_kbtu_sf0.105-0.0040.0780.1980.2350.1680.117-0.5270.8750.8580.9981.0000.6230.6160.464-0.106-0.0510.029-0.0400.0080.1160.2560.0160.0400.0000.0690.0280.028
siteenergyuse_kbtu0.1470.0520.2800.7660.3020.7520.732-0.1750.7020.6910.6290.6231.0000.9990.877-0.118-0.0940.019-0.160-0.2940.1350.2900.0420.1970.0000.0450.0000.000
siteenergyusewn_kbtu0.1430.0530.2730.7630.2970.7490.731-0.1760.7040.6950.6200.6160.9991.0000.887-0.118-0.0960.020-0.150-0.3130.1420.2990.0410.2130.0000.0470.0000.000
totalghgemissions0.1200.0450.1680.5810.2050.5770.568-0.1000.7100.7160.4610.4640.8770.8871.000-0.128-0.1120.027-0.025-0.6640.1160.2670.0000.1810.0000.0360.0000.000
zipcode-0.1930.016-0.227-0.091-0.125-0.078-0.053-0.003-0.128-0.126-0.105-0.106-0.118-0.118-0.1281.000-0.0480.007-0.0890.0620.0530.0740.2550.1470.0000.0860.0640.064
latitude0.5140.0510.068-0.0560.018-0.065-0.0480.085-0.082-0.084-0.051-0.051-0.094-0.096-0.112-0.0481.000-0.028-0.1360.0830.1530.2150.5900.3020.0000.1540.1420.142
longitude-0.3500.038-0.115-0.022-0.050-0.017-0.012-0.0340.0430.0450.0270.0290.0190.0200.0270.007-0.0281.0000.050-0.0460.1270.1470.4890.1670.0000.0650.1990.199
age-0.003-0.038-0.293-0.315-0.240-0.286-0.293-0.0820.0660.088-0.060-0.040-0.160-0.150-0.025-0.089-0.1360.0501.000-0.1990.1590.1880.1780.1510.0000.3410.0470.047
source_site0.013-0.0100.130-0.0310.063-0.042-0.043-0.019-0.403-0.4340.0220.008-0.294-0.313-0.6640.0620.083-0.046-0.1991.0000.0760.1540.0510.0860.5280.5270.0740.074
buildingtype0.1490.1830.2640.1260.0520.1290.1190.1190.1330.1380.1130.1160.1350.1420.1160.0530.1530.1270.1590.0761.0000.7290.1990.1930.0000.2910.7570.757
primarypropertytype0.2510.2010.2780.1940.1540.1920.1970.1210.2790.2710.2450.2560.2900.2990.2670.0740.2150.1470.1880.1540.7291.0000.2430.2910.1420.3560.6320.632
neighborhood0.8790.0360.1400.0660.0600.0630.0640.0560.0560.0550.0270.0160.0420.0410.0000.2550.5900.4890.1780.0510.1990.2431.0000.2880.0000.1580.1500.150
steam0.2150.0000.2960.2030.0830.2300.2250.0140.1220.1210.0390.0400.1970.2130.1810.1470.3020.1670.1510.0860.1930.2910.2881.0000.0000.0000.0160.016
electricity0.0000.0000.0000.0000.0000.0000.0000.0000.0870.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.5280.0000.1420.0000.0001.0000.0060.0000.000
naturalgas0.1390.0000.0440.0900.0100.0880.0940.1010.1530.1750.0700.0690.0450.0470.0360.0860.1540.0650.3410.5270.2910.3560.1580.0000.0061.0000.0350.035
defaultdata0.0980.0000.0000.0000.0000.0000.0000.1110.0500.0570.0250.0280.0000.0000.0000.0640.1420.1990.0470.0740.7570.6320.1500.0160.0000.0351.0000.995
compliancestatus0.0980.0000.0000.0000.0000.0000.0000.1110.0500.0570.0250.0280.0000.0000.0000.0640.1420.1990.0470.0740.7570.6320.1500.0160.0000.0350.9951.000

Missing values

2023-08-03T23:01:10.451253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-03T23:01:11.173437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-03T23:01:11.542719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

buildingtypeprimarypropertytypetaxparcelidentificationnumbercouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresiteeui_kbtu_sfsiteeuiwn_kbtu_sfsourceeui_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtusteamelectricitynaturalgasdefaultdatacompliancestatustotalghgemissionszipcodelatitudelongitudeagesource_site
0NonResidentialHotel06590000307DOWNTOWN11288434088434HotelHotel88434.060.081.69999784.300003182.500000189.0000007226362.57456910.0TrueTrueTrueFalseCompliant249.9898101.047.61220-122.33799962.241993
1NonResidentialHotel06590002207DOWNTOWN1111035661506488502Hotel, Parking, RestaurantHotel83880.061.094.80000397.900002176.100006179.3999948387933.08664479.0FalseTrueTrueFalseCompliant295.8698101.047.61317-122.33393271.832482
2NonResidentialHotel06590004757DOWNTOWN141956110196718759392HotelHotel756493.043.096.00000097.699997241.899994244.10000672587024.073937112.0TrueTrueTrueFalseCompliant2089.2898101.047.61393-122.33810542.498465
3NonResidentialHotel06590006407DOWNTOWN11061320061320HotelHotel61320.056.0110.800003113.300003216.199997224.0000006794584.06946800.5TrueTrueTrueFalseCompliant286.4398101.047.61412-122.33664971.977052
4NonResidentialHotel06590009707DOWNTOWN11817558062000113580Hotel, Parking, Swimming PoolHotel123445.075.0114.800003118.699997211.399994215.60000614172606.014656503.0FalseTrueTrueFalseCompliant505.0198121.047.61375-122.34047431.816344
5Nonresidential COSOther06600005607DOWNTOWN12972883719860090Police StationPolice Station88830.0NaN136.100006141.600006316.299988320.50000012086616.012581712.0FalseTrueTrueFalseCompliant301.8198101.047.61623-122.33657242.263418
6NonResidentialHotel06600008257DOWNTOWN11183008083008HotelHotel81352.027.070.80000374.500000146.600006154.6999975758795.06062767.5FalseTrueTrueFalseCompliant176.1498101.047.61390-122.33283972.076510
7NonResidentialOther06600009557DOWNTOWN181027610102761Other - Entertainment/Public AssemblyOther - Entertainment/Public Assembly102761.0NaN61.29999968.800003141.699997152.3000036298131.57067881.5TrueTrueTrueFalseCompliant221.5198101.047.61327-122.33136972.213663
8NonResidentialHotel09390000807DOWNTOWN1151639840163984HotelHotel163984.043.083.69999786.599998180.899994187.19999713723820.014194054.0FalseTrueTrueFalseCompliant392.1698104.047.60294-122.332631192.161663
9Multifamily MR (5-9)Mid-Rise Multifamily09390001057DOWNTOWN1663712149662216Multifamily HousingMultifamily Housing56132.01.081.50000085.599998182.699997187.3999944573777.04807679.5TrueTrueTrueFalseCompliant151.1298104.047.60284-122.331841132.189252
buildingtypeprimarypropertytypetaxparcelidentificationnumbercouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresiteeui_kbtu_sfsiteeuiwn_kbtu_sfsourceeui_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtusteamelectricitynaturalgasdefaultdatacompliancestatustotalghgemissionszipcodelatitudelongitudeagesource_site
3295Nonresidential COSOffice24250391377MAGNOLIA / QUEEN ANNE1113661013661OfficeOffice13661.075.036.79999940.900002115.500000128.3999945.026677e+055.585251e+05FalseTrueFalseTrueError - Correct Default Data3.5098119.247.63572-122.37525713.139364
3296Nonresidential COSOther29250490873EAST1123445023445Other - RecreationOther - Recreation23445.0NaN254.899994286.500000380.100006413.2000125.976246e+066.716330e+06FalseTrueTrueFalseCompliant259.2298106.047.63228-122.315741111.442234
3297Nonresidential COSMixed Use Property75448002453CENTRAL1120050020050Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/ScienceOther - Recreation8108.0NaN90.40000299.400002175.199997184.6000061.813404e+061.993137e+06FalseTrueTrueFalseCompliant60.8198126.447.60775-122.30225291.857143
3298Nonresidential COSOffice41543005852SOUTHEAST1115398015398OfficeOffice15398.093.025.20000126.90000064.09999866.6999973.878100e+054.141724e+05FalseTrueTrueTrueError - Correct Default Data7.7998120.647.56440-122.27813632.479554
3299Nonresidential COSOther25240390591DELRIDGE1118261018261Other - RecreationOther - Recreation18261.0NaN51.00000056.200001126.000000136.6000069.320821e+051.025432e+06FalseTrueTrueFalseCompliant20.3398126.047.54067-122.37441412.430605
3300Nonresidential COSOffice16240490802GREATER DUWAMISH1112294012294OfficeOffice12294.046.069.09999876.699997161.699997176.1000068.497457e+059.430032e+05FalseTrueTrueTrueError - Correct Default Data20.9498126.047.56722-122.31154332.295958
3301Nonresidential COSOther35583000002DOWNTOWN1116000016000Other - RecreationOther - Recreation16000.0NaN59.40000265.900002114.199997118.9000029.502762e+051.053706e+06FalseTrueTrueFalseCompliant32.1798113.047.59625-122.32283191.804249
3302Nonresidential COSOther17945011507MAGNOLIA / QUEEN ANNE1113157013157Fitness Center/Health Club/Gym, Other - Recreation, Swimming PoolOther - Recreation7583.0NaN438.200012460.100006744.799988767.7999885.765898e+066.053764e+06FalseTrueTrueFalseCompliant223.5498112.047.63644-122.35784491.668768
3303Nonresidential COSMixed Use Property78836031551GREATER DUWAMISH1114101014101Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation6601.0NaN51.00000055.500000105.300003110.8000037.194712e+057.828413e+05FalseTrueTrueFalseCompliant22.1198108.047.52832-122.32431341.996396
3304Nonresidential COSMixed Use Property78570020302GREATER DUWAMISH1118258018258Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation8271.0NaN63.09999870.900002115.800003123.9000021.152896e+061.293722e+06FalseTrueTrueFalseCompliant41.2798118.747.53939-122.29536851.747532